AI Engineering
by Chip Huyen · first published 2024
67% recommend33% neutral
Why HN recommends it
Practical guide to building applications on foundation models, focused on engineering rather than model theory. It provides a fundamental grounding in LLM work and covers weighing RAG versus finetuning.
Summarised by Claude from recommending comments
Recent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models. The book starts with an overview of AI engineering,
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…that they are available (library/models/whatever), how to build your apps using them; 1) Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen. 2) AI Engineering: Building Applications with Foundation Models by Chip Huyen (this is a very recent book). 3) Generative Deep Learning: Teaching Machines To Paint, Write, Compose, and Play by David Foster. 4) Building…
on “AI isn't going to kill the software industry” ↗This is not a complete solution, but two books you should buy immediately: Designing Data-Intensive Applications by Martin Kleppmann AI Engineering by Chip Huyen
on “Ask HN: How would you learn AI-assisted development from the ground up?” ↗…papers. The latest Bishop’s "Deep Learning: Foundations and Concepts (2024)" [1] is an excellent resource that covers the "basics" of deep learning and is quite updated. Another good option is Chip Huyen’s "AI Engineering (2024)" [2]. Another excellent choice will be "Dive into Deep Learning" [3], Understanding Deep Learning [4], or just read anything from fast.ai and watch Karpathy's lectures on YouTube. [1]: https://www.bishopbook.com…
on “AI Engineer Reading List” ↗…but I've seen enough of them that I'm confident suggesting them: - Prompt Engineering for LLMs by John Berryman and Albert Ziegler: https://www.amazon.com/Prompt-Engineering-LLMs-Model-Based-A... - AI Engineering by Chip Huyen, which I recommend based on the strength of this extract about "agents": https://huyenchip.com/2025/01/07/agents.html
on “AI isn't going to kill the software industry” ↗I recently did a deep dive into Chip Huyen's "AI Engineering" and one argument stood out as particularly crucial for teams building with LLMs today. It's the clear, principled distinction between Retrieval-Augmented Generation (RAG) and finetuning. Many engineering…
on “Chip Huyen's 'AI Engineering' Deep Dive” ↗Absolutely; I'd check out AI Engineering by Chip Huyen to get a fundamental understanding of the space and do a couple of side projects in the area to get familiar and be able to speak to the nuances of working with LLMs, and that will be enough…
on “The AI job market in 2026” ↗…rarely get stuck on a bug, only slowed down. I can rapidly prototype an idea, and then take it all the way to development, qa, and deployment, given the right resources. 1. In line with Chip Huyen's AI Engineering book. ISBN 978-1098166304
on “Ask HN: Anyone is an "AI Engineer"? What does your job tasks include?” ↗…systems and LLM agents. It includes: - System design patterns for agent architectures - A comprehensive OpenAI Agents SDK guide (with code) - Summaries of tools and design resources - A summary of the AI Engineering book by Chip Huyen - Hands-on code examples and projects - More frameworks and real-world projects coming soon
on “Agentic AI Systems – Tools, System Design, and Resources for AI Agents” ↗…conceptually AFAICT. 2. Build an LLM from Scratch, by Sebastian Raschka (https://www.manning.com/books/build-a-large-language-model-from-scratch). Seems in-depth. But I don't really want to build an LLM. 3. AI Engineering, by Chip Huyen (https://www.amazon.in/AI-Engineering-Building-Applications-Foundation/dp/1098166302). This seems promising, although it is not coding focussed. Perhaps something like How Claude Code…
on “Ask HN: Resources for a conceptual model of LLMs as applicable to coding?” ↗